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Generation of daily solar irradiation by means of artificial neural net works

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  • Siqueira, Adalberto N.
  • Tiba, Chigueru
  • Fraidenraich, Naum

Abstract

The present study proposes the utilization of Artificial Neural Networks (ANN) as an alternative for generating synthetic series of daily solar irradiation. The sequences were generated from the use of daily temporal series of a group of meteorological variables that were measured simultaneously. The data used were measured between the years of 1998 and 2006 in two temperate climate localities of Brazil, Ilha Solteira (São Paulo) and Pelotas (Rio Grande do Sul). The estimates were taken for the months of January, April, July and October, through two models which are distinguished regarding the use or nonuse of measured bright sunshine hours as an input variable. An evaluation of the performance of the 56 months of solar irradiation generated by way of ANN showed that by using the measured bright sunshine hours as an input variable (model 1), the RMSE obtained were less or equal to 23.2% being that of those, although 43 of those months presented RMSE less or equal to 12.3%. In the case of the model that did not use the measured bright sunshine hours but used a daylight length (model 2), RMSE were obtained that varied from 8.5% to 37.5%, although 38 of those months presented RMSE less or equal to 20.0%.

Suggested Citation

  • Siqueira, Adalberto N. & Tiba, Chigueru & Fraidenraich, Naum, 2010. "Generation of daily solar irradiation by means of artificial neural net works," Renewable Energy, Elsevier, vol. 35(11), pages 2406-2414.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:11:p:2406-2414
    DOI: 10.1016/j.renene.2010.03.019
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    References listed on IDEAS

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    1. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    2. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
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    1. Yacef, R. & Benghanem, M. & Mellit, A., 2012. "Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study," Renewable Energy, Elsevier, vol. 48(C), pages 146-154.
    2. Urraca, R. & Martinez-de-Pison, E. & Sanz-Garcia, A. & Antonanzas, J. & Antonanzas-Torres, F., 2017. "Estimation methods for global solar radiation: Case study evaluation of five different approaches in central Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1098-1113.
    3. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.

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